Machine-Learning Assessment of Chlorophyll-a Responses to Atmospheric Dust and Environmental Factors Using Remote Sensing Data in the Marmara Sea

The Marmara Sea, covering approximately 11,350 km² in northwestern Turkey, links the Black Sea and the Aegean Sea via the Bosporus and Dardanelles straits. It is bordered by densely populated and industrialized cities such as Istanbul. The Marmara Sea is facing eutrophication and mucilage outbreaks, necessitating the monitoring of key indicators, including chlorophyll-a, which serves as an indicator of phytoplankton abundance. Atmospheric dust deposition can play a significant role in providing nutrients such as nitrogen, phosphorus, silica, and iron to the surface ocean, thereby affecting phytoplankton growth. Excessive phytoplankton growth and the accumulation of organic matter trigger mucilage formation under suitable conditions. The region is influenced by dust transported from regional and distant sources, such as the Sahara Desert.

In this study, spatio-temporal dynamics of chlorophyll-a (Chl-a), Aerosol Optical Depth (AOD), Sea Surface Temperature (SST), Particulate Organic Carbon (POC), Photosynthetically Active Radiation (PAR), and precipitation were investigated on a monthly scale using MODIS-derived products from 2005 to 2020. Time series analysis and machine learning models such as HGB (Histogram Gradient Boosting), Random Forest, and Multiple Linear Regression were performed for exploring temporal patterns, relationships, and modeling Chl-a, respectively. Chl-a showed a moderate negative correlation with SST (r = –0.52) and a strong positive correlation with POC (r = 0.80), while its relationship with AOD was negligible. It should be noted that during desert dust episodes, a significant lagged correlation was observed between Chl-a and AOD. The observed Chl-a values ranged between 0.6 and 19.50 mg/m³ over the study period, with the highest values observed in April and the lowest values occurring between June and November. Modeling Chl-a based on satellite-derived environmental variables showed that the Histogram Gradient Boosting algorithm achieved the highest performance, yielding r = 0.807, R² = 0.645, RMSE = 1.870, MAE = 1.218, and MBE = 0.062. These results highlighted the strong influence of SST and POC on Chl-a variability, while AOD appears to have minimal direct impact. Further investigation of the impact of the high dust deposition periods during dust storm events is suggested for the Marmara Sea.